Graph-Aware Unsupervised Feature Scoring Using Reconstruction Errors in GNN Embeddings

Abstract

Graph neural networks (GNNs) have recently gained significant attention for their ability to model and analyze complex relationships, leading to numerous applications in a variety of fields. In line with this, explainable models such as GNNExplainer have been developed to provide insights into how GNN models make decisions. However, much of the research has focused on identifying important edges and subgraphs, with relatively less emphasis on feature importance. To address this gap, we propose a novel unsupervised feature scoring method aimed at extracting features that are well-aligned with graph structures. Our approach leverages the distinction between features that explain the overall structural characteristics of the graph and those that do not. Through comparative experiments with other algorithms, we demonstrated that our proposed method is effective in selecting important features in unsupervised settings. Additionally, empirical analysis of gene expression data confirmed that our method could aid in biomarker discovery, highlighting its potential for practical applications in biological studies.